{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2019 The Google Research Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "     http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corrupted Sample Discovery & Robust Learning using DVRL\n",
    "\n",
    " * Jinsung Yoon, Sercan O Arik, Tomas Pfister, \"Data Valuation using Reinforcement Learning\", arXiv preprint arXiv:1909.11671 (2019) - https://arxiv.org/abs/1909.11671\n",
    "\n",
    "This notebook describes the user-guide of corrupted sample discovery and robust learning applications using \"Data Valuation using Reinforcement Learning (DVRL)\". \n",
    "\n",
    "There are some scenarios where training samples may contain corrupted samples, e.g. due to cheap label collection methods. An automated corrupted sample discovery method would be highly beneficial for distinguishing samples with clean vs. noisy labels. Data valuation can be used in this setting by having a small clean validation set to assign low data values to the potential samples with noisy labels. With an optimal data value estimator, all noisy labels would get the lowest data values. \n",
    "\n",
    "DVRL can also reliably learn with noisy data in an end-to-end way. Ideally, noisy samples should get low data values as DVRL converges and a high performance model can be returned.\n",
    "\n",
    "You need:\n",
    "\n",
    "**Training set** (low-quality data (e.g. noisy data)) / **Validation set** (high-quality data (e.g. clean data)) / **Testing set** (high-quality data (e.g. clean data)) \n",
    " * If there is no explicit validation set, you can split a small portion of testing set as the validation set. \n",
    " * Note that training set does not have to be low quality for DVRL; however, in this notebook, we use a low quality training set for a more clear demonstration as the samples are easier to distinguish in terms of their value.\n",
    " * If you have your own training / validation / testing datasets, you can put them under './repo/data_files/' directory with 'train.csv', 'valid.csv', 'test.csv' names.\n",
    " * In this notebook, we use adult income dataset (https://archive.ics.uci.edu/ml/datasets/Adult) as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Prerequisite\n",
    "\n",
    " * Download lightgbm package.\n",
    " * Clone https://github.com/google-research/google-research.git to the current directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uses pip3 to install necessary package (lightgbm)\n",
    "!pip3 install lightgbm\n",
    "\n",
    "# Resets the IPython kernel to import the installed package.\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from git import Repo\n",
    "\n",
    "# Current working directory\n",
    "repo_dir = os.getcwd() + '/repo'\n",
    "\n",
    "if not os.path.exists(repo_dir):\n",
    "    os.makedirs(repo_dir)\n",
    "\n",
    "# Clones github repository\n",
    "if not os.listdir(repo_dir):\n",
    "    git_url = \"https://github.com/google-research/google-research.git\"\n",
    "    Repo.clone_from(git_url, repo_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Necessary packages and functions call\n",
    "\n",
    " * load_tabular_data: Data loader for tabular datasets.\n",
    " * data_preprocess: Data extraction and normalization.\n",
    " * dvrl_classification: Data valuation function for classification problem.\n",
    " * metrics: Evaluation metrics of the quality of data valuation in various metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn import linear_model\n",
    "import lightgbm\n",
    "\n",
    "# Sets current directory\n",
    "os.chdir(repo_dir)\n",
    "\n",
    "from dvrl.data_loading import load_tabular_data, preprocess_data\n",
    "from dvrl import dvrl\n",
    "from dvrl.dvrl_metrics import discover_corrupted_sample, remove_high_low, learn_with_dvrl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading & Sample corruption\n",
    "\n",
    " * Create training dataset, validation and testing datasets, and save as train.csv, valid.csv, test.csv under './repo/data_files/' directory.\n",
    " * In this notebook, we corrupt a certain portion of samples in training set to create \"artificially\" low-quality data.\n",
    " * If you have your own train.csv (low-quality data), valid.csv (ideally high-quality data), test.csv (ideally similar to validation distribution), you can skip this cell and just save those files to './repo/data_files/' directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished data loading.\n"
     ]
    }
   ],
   "source": [
    "# Data name: 'adult' in this notebook\n",
    "data_name = 'adult'\n",
    "\n",
    "# The number of training and validation samples\n",
    "dict_no = dict()\n",
    "dict_no['train'] = 1000\n",
    "dict_no['valid'] = 400\n",
    "\n",
    "# Label noise ratio\n",
    "noise_rate = 0.2\n",
    "\n",
    "# Loads data and corrupts labels\n",
    "noise_idx = load_tabular_data(data_name, dict_no, noise_rate)\n",
    "# noise_idx: ground truth noisy sample indices\n",
    "\n",
    "print('Finished data loading.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    " * Extract features and labels from train.csv, valid.csv, test.csv in './repo/data_files/' directory.\n",
    " * Normalize the features of training, validation, and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished data preprocess.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/google/home/jinsungyoon/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# Normalization methods: 'minmax' or 'standard'\n",
    "normalization = 'minmax' \n",
    "\n",
    "# Extracts features and labels. Then, normalizes features.\n",
    "x_train, y_train, x_valid, y_valid, x_test, y_test, _ = \\\n",
    "preprocess_data(normalization, 'train.csv', 'valid.csv', 'test.csv')\n",
    "\n",
    "print('Finished data preprocess.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run DVRL\n",
    "\n",
    "1. **Input**: \n",
    "\n",
    " * data valuator network parameters: Set network parameters of data valuator.\n",
    " * pred_model: The predictor model that maps output from the input. Any machine learning model (e.g. a neural network or ensemble decision tree) can be used as the predictor model, as long as it has fit, and predict (for regression)/predict_proba (for classification) as its subfunctions. Fit can be implemented using multiple backpropagation iterations.\n",
    " \n",
    " \n",
    "2. **Output**:\n",
    " * data_valuator: Function that uses training set as inputs to estimate data values.\n",
    " * dvrl_predictor: Function that predicts labels of the testing samples.\n",
    " * dve_out: Estimated data values for all training samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/google/home/jinsungyoon/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [02:12<00:00, 15.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished dvrl training.\n",
      "WARNING:tensorflow:From /usr/local/google/home/jinsungyoon/anaconda3/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./tmp/model.ckpt\n",
      "Finished data valuation.\n"
     ]
    }
   ],
   "source": [
    "# Resets the graph\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# Network parameters\n",
    "parameters = dict()\n",
    "parameters['hidden_dim'] = 100\n",
    "parameters['comb_dim'] = 10\n",
    "parameters['iterations'] = 2000\n",
    "parameters['activation'] = tf.nn.relu\n",
    "parameters['layer_number'] = 5\n",
    "parameters['batch_size'] = 2000\n",
    "parameters['learning_rate'] = 0.01\n",
    "\n",
    "# Sets checkpoint file name\n",
    "checkpoint_file_name = './tmp/model.ckpt'\n",
    "\n",
    "# Defines predictive model\n",
    "pred_model = linear_model.LogisticRegression(solver='lbfgs')\n",
    "problem = 'classification'\n",
    "\n",
    "# Flags for using stochastic gradient descent / pre-trained model\n",
    "flags = {'sgd': False, 'pretrain': False}\n",
    "\n",
    "# Initalizes DVRL\n",
    "dvrl_class = dvrl.Dvrl(x_train, y_train, x_valid, y_valid, \n",
    "                       problem, pred_model, parameters, checkpoint_file_name, flags)\n",
    "\n",
    "# Trains DVRL\n",
    "dvrl_class.train_dvrl('auc')\n",
    "\n",
    "print('Finished dvrl training.')\n",
    "\n",
    "# Estimates data values\n",
    "dve_out = dvrl_class.data_valuator(x_train, y_train)\n",
    "\n",
    "# Predicts with DVRL\n",
    "y_test_hat = dvrl_class.dvrl_predictor(x_test)\n",
    "\n",
    "print('Finished data valuation.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluations\n",
    "\n",
    " * In this notebook, we use LightGBM as the predictive model in DVRL (but we can also replace it with another method for evaluation purposes.\n",
    " * Here, we use average accuracy as the performance metric (we can also replace with other metrics like AUC, see metrics.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Robust learning\n",
    "\n",
    "DVRL learns robustly although the training data contains low quality/noisy samples, using the guidance from the high quality/clean validation data via reinforcement learning.\n",
    " * Train predictive model with weighted optimization using estimated data values by DVRL as the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DVRL-weighted learning performance: 0.8272\n"
     ]
    }
   ],
   "source": [
    "# Defines evaluation model\n",
    "eval_model = lightgbm.LGBMClassifier()\n",
    "\n",
    "# Robust learning (DVRL-weighted learning)\n",
    "robust_perf = learn_with_dvrl(dve_out, eval_model, \n",
    "                              x_train, y_train, x_valid, y_valid, x_test, y_test, 'accuracy')\n",
    "\n",
    "print('DVRL-weighted learning performance: ' + str(np.round(robust_perf, 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Removing high/low valued samples\n",
    "\n",
    "Removing low value samples from the training dataset can improve the predictor model performance, especially in the cases where the training dataset contains corrupted samples. On the other\n",
    "hand, removing high value samples, especially if the dataset is small, would decrease the performance significantly. Overall, the performance after removing high/low value samples is a strong\n",
    "indicator for the quality of data valuation.\n",
    "\n",
    "DVRL can rank the training data samples according to their estimated data value, and **by removing the low value samples we can significantly improve performance, whereas removing the high value samples degrades the performance severely. Thus for a high performance data valuation method, a large gap is expected in the performance curves with removal of high vs. low value samples**\n",
    " * Train predictive models after removing certain portions of high/low valued training samples.\n",
    " * Visualize the results using line graphs (set plot = True).\n",
    " * x-axis: Portions of removed samples.\n",
    " * y-axis: Prediction performance (accuracy).\n",
    " * Blue line: Removing low value data, Orange line: Removing high value data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluates performance after removing high/low valued samples\n",
    "remove_high_low_performance = remove_high_low(dve_out, eval_model, x_train, y_train, \n",
    "                                              x_valid, y_valid, x_test, y_test, 'accuracy', plot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Corrupted sample discovery\n",
    "\n",
    "For our synthetically-generated noisy training dataset, we can assess the performance of our method in **finding the noisy samples by using the known noise indices**. Note that unlike the first two evaluations, this cell is only for academic purposes because you need the ground truth noisy sample indices so if users come with their own .csv files, they cannot use this cell. \n",
    "\n",
    " * Report True Positive Rates (TPR) of corrupted sample discovery.\n",
    " * Visualize the results using line graphs (set plot = True).\n",
    " * x-axis: Portions of inspected samples.\n",
    " * y-axis: True positive rates (TPR) of corrupted sample discovery.\n",
    " * Blue line: DVRL, Orange line: Optimal, Green line: Random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If noise_rate is positive value.\n",
    "if noise_rate > 0:\n",
    "    \n",
    "    # Evaluates true positive rates (TPR) of corrupted sample discovery and plot TPR\n",
    "    noise_discovery_performance = discover_corrupted_sample(dve_out, noise_idx, noise_rate, plot = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
